Adopting incentive mechanisms for large-scale participation in mobile crowdsensing: from literature review to a conceptual framework
Ogie Hum. Cent. Comput. Inf. Sci.
Adopting incentive mechanisms for large‑scale participation in mobile crowdsensing: from literature review to a conceptual framework
R. I. Ogie
Mobile crowdsensing is a burgeoning concept that allows smart cities to leverage the sensing power and ubiquitous nature of mobile devices in order to capture and map phenomena of common interest. At the core of any successful mobile crowdsensing application is active user participation, without which the system is of no value in sensing the phenomenon of interest. A major challenge militating against widespread use and adoption of mobile crowdsensing applications is the issue of how to identify the most appropriate incentive mechanism for adequately and efficiently motivating participants. This paper reviews literature on incentive mechanisms for mobile crowdsensing and proposes the concept of SPECTRUM as a guide for inferring the most appropriate type of incentive suited to any given crowdsensing task. Furthermore, the paper highlights research challenges and areas where additional studies related to the different factors outlined in the concept of SPECTRUM are needed to improve citizen participation in mobile crowdsensing. It is envisaged that the broad range of factors covered in SPECTRUM will enable smart cities to efficiently engage citizens in large-scale crowdsensing initiatives. More importantly, the paper is expected to trigger empirical investigations into how various factors as outlined in SPECTRUM can influence the type of incentive mechanism that is considered most appropriate for any given mobile crowdsensing initiative.
Mobile; Crowd sensing; Monetary; Incentive; Incentive mechanism; Participatory sensing; Urban sensing; Smartphone; Mobile crowdsensing; Community sensing
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With rapid growth in mobile technologies, smart devices such as mobile phones, smart
vehicles and wearables are fast becoming powerful sensing units used at a societal scale
for monitoring the surrounding environment and for understanding complex urban and
community dynamics [1]. These devices come equipped with a broad range of
sophisticated embedded sensors such as an accelerometer, gyroscope, GNSS, digital compass,
GPS, microphone, light intensity sensor and camera [2]. The phenomenal growth in
the richness and diversity of sensors on smart devices [3], combined with the inherent
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mobility of mobile device users provide a unique opportunity to harvest large-scale
sensing data with fine-grained spatio-temporal coverage [1, 4]. The process for doing this is
commonly referred to as mobile crowdsensing, a socio-technical concept facilitated by a
growing number of software applications that are fast becoming indispensable tools for
active urban intervention [1].
Mobile crowdsensing can be formally defined as a large-scale sensing paradigm in
which spatially distributed participants with sensing and computing devices capture and
collectively share data in order to measure and map phenomena of common interest [3].
Unlike the traditional static infrastructure-based sensing method, mobile
crowdsensing does not require the deployment of expensive fixed infrastructure assets, potentially
making it a cheaper solution [1]. Basically, the key drivers of mobile crowdsensing are
the ubiquitous mobile device users, whose geographical distribution allows for an
extensive acquisition of spatially-oriented data in a scale that steers various smart-city
applications [1].
Applications of mobile crowdsensing cut across a wide range of areas that are critical
for sustainable urban development and for improvement to quality of life for citizens, in
terms of convenience, comfort, safety and security [1]. Typical application areas include
environment monitoring, community healthcare, surveys with embedded geotagged
photos [5], traffic monitoring and transportation planning, garbage classification,
infrastructure management, disaster management, public safety and so on [1, 6, 7]. With the
recent surge in the application of these socio-technical systems in city management,
mobile crowdsensing has been recognised as an important technological enabler for
smart cities [1]. In this sense, successful societies are increasingly incorporating data and
insight from mobile crowdsensing solutions into their planning process, decision
making and policy formation activities [1]. Consequently, this burgeoning phenomenon has
attracted significant attention from major industry players and academic research
communities, seeking to address the key challenges militating again (...truncated)